Non-Gaussian Narrow-Band Random Fatigue

[+] Author and Article Information
R. D. Blevins

Goodrich Aerostructures, Inc., Mail Stop 107P, 850 Lagoon Drive, Chula Vista, CA 91910

J. Appl. Mech 69(3), 317-324 (May 03, 2002) (8 pages) doi:10.1115/1.1428332 History: Received January 13, 2000; Revised November 13, 2000; Online May 03, 2002
Copyright © 2002 by ASME
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Grahic Jump Location
Effect of mean stress on M=6 finite random noise induced fatigue of Al 2024-T3 with Kt=1.5. Note RMS stress refers to oscillating component only.
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Effect of increasing randomness of fatigue of Al 2024-T3 with Kt=1.5 and no mean stress. N is number of randomly phased sine waves in the process.
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Sinusoidal and random fatigue data for austenitic stainless steel 321 in comparison with exponential fatigue law fits and predictions. Random fit is identical to M=5 prediction.
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Fractional (Eq. (21)) and cumulative damage with Rayleigh distribution. Curves have been normalized to a maximum of unity. Material is Al 7075-T6, Kt=2,b=6.15.
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Probability density of amplitude for sum of randomly phased sine waves (Table 2)
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Fatigue curves for 2024-T3 Al Kt=1.5 with no mean stress and sinusoidal cycling
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Time history of a narrow-band random process with mean stress (Sm) and the associated probability density of the peaks. Process shown is the sum of two sine waves.



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